Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

Antiamyloid antibodies have been used to reduce cerebral amyloid-beta (Aβ) load in
patients with Alzheimer's disease. We applied focused ultrasound with each of six monthly …

Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

Recent work has shown that label-efficient few-shot learning through self-supervision can
achieve promising medical image segmentation results. However, few-shot segmentation …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks
reproducibility between protocols and scanners. It has been shown that even when care is …

Transformer-based models, capable of learning better global dependencies, have recently
demonstrated exceptional representation learning capabilities in computer vision and …

In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes
pulse sequence-based contrast variations in MR images from site to site, which impedes …